3 papers
cs.RO2025
RAPTOR: A Foundation Policy for Quadrotor Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies train…
cs.RO2024
Data-Driven System Identification of Quadrotors Subject to Motor Delays
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In con…
cs.RO2023
Learning to Fly in Seconds
Jonas Eschmann, Dario Albani, Giuseppe Loianno
Learning-based methods, particularly Reinforcement Learning (RL), hold great promise for streamlining deployment, enhancing performance, and achieving generalization in the control…